Mathematical graphs which are made up of nodes and edges are pervasive in day to day life. Graphs are even more essential for analysts that rely on graph based data for analyzing domains such as social networks, computer networks, road networks, subway maps and command and control structures. This makes graph visualization and understanding pivotal to effectively using these potentially large and complex graph based data sources.
Traditional graph visualization uses one or more graph layout algorithms to draw rectangles and lines to depict nodes and edges in the graph. These visualizations often rely on algorithms that attempt to layout the graph using poorly balanced aesthetic principles. While the readability of the graphs is the principle purpose of these layout algorithms, increasing graph size and complexity are reducing the effectiveness of these algorithms to allow the user to quickly and easily digest both the structure and the content of these graphs. This problem is further exacerbated for graphs where the number of nodes greatly exceeds the display area.
Traditional graph visualizations also often fail to maintain the gestalt principle of proximity where the viewer automatically correlates graph elements' proximity to some form of relationship between those elements. Another failing of traditional graph visualizations is that they are ill-suited to address rapid sequential questions where each layout that optimizes a particular question can often cause the entire display to change radically. A layout optimizing a single path through a graph may omit values at the nodes; another layout that bundles edges to give the overall flow within a graph makes it impossible to see which paths actually exist. Overall, each traditional layout compromises which aspects of a graph is displayed.